Abstract
Human action classification is a dynamic research topic in computer vision and has applications in video surveillance, human–computer interaction, and sign-language recognition. This paper aims to present an approach for the categorization of depth video oriented human action. In the approach, the enhanced motion and static history images are computed and a set of 2D auto-correlation gradient feature vectors is obtained from them to describe an action. Kernel-based Extreme Learning Machine is used with the extracted features to distinguish the diverse action types promisingly. The proposed approach is thoroughly assessed for the action datasets namely MSRAction3D, DHA, and UTD-MHAD. The approach achieves an accuracy of 97.44% for MSRAction3D, 99.13% for DHA, and 88.37% for UTD-MHAD. The experimental results and analysis demonstrate that the classification performance of the proposed method is considerable and surpasses the state-of-the-art human action classification methods. Besides, from the complexity analysis of the approach, it is turn out that our method is consistent for the real-time operation with low computational complexity.
Highlights
A large number of researchers have been attracted to human action classification problem due to its wide range of real-world applications
This paper has introduced an efficacious and efficient human action framework based on enhanced auto-correlation features
The system uses the 3D Motion Trail Model (3DMTM) to derive three motion information images and three motionless information images from an action video. Those motion and static information-oriented maps are improved by engaging the Local Binary Pattern (LBP) algorithm on them
Summary
A large number of researchers have been attracted to human action classification problem due to its wide range of real-world applications. A large number of researchers introduced several action or activity recognition model by using data sensors like RGB video cameras [10], depth video cameras [2], and wearable sensors [11]. Among these two video data sources, action recognition research based on conventional RGB cameras Utilization of RGB cameras for action recognition raises significant impediments such as lighting variations and cluttered background [13]. Depth cameras generate depth images, which are insensitive to lighting variations and make background subtraction and segmentation easier. We can obtain body shape and structure characteristics and the human skeleton information from depth images
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